Comprehensive Assessment of MRI-based Artificial Intelligence Frameworks Performance in the Detection, Segmentation, and Classification of Prostate Lesions Using Open-Source Databases

IF 2.4 4区 医学 Q2 UROLOGY & NEPHROLOGY
Lorenzo Storino Ramacciotti MD , Jacob S. Hershenhouse BS , Daniel Mokhtar BS , Divyangi Paralkar MD , Masatomo Kaneko MD, PhD , Michael Eppler BA , Karanvir Gill BA , Vasileios Mogoulianitis MS , Vinay Duddalwar MD , Andre L. Abreu MD , Inderbir Gill MD , Giovanni E. Cacciamani MSc, MD, FEBU
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引用次数: 0
使用开源数据库对基于MRI的人工智能框架在前列腺病变检测、分割和分类中的性能进行综合评估。
许多基于MRI的人工智能(AI)框架已被设计用于通过MRI对癌症前列腺病变进行检测、分割和分类,这是传统解释固有的阅读器内和阅读器间变异的结果。发布开源数据集的目的是为自动化或半自动化任务中的各种人工智能框架的测试提供免费可用的MRI。在这里,使用开源数据库对基于MRI的人工智能框架用于检测、分割和分类前列腺病变的性能进行了深入评估。在17个数据集中,12个数据集是前列腺癌症检测/分类的特异性数据集,52项研究符合纳入标准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Urologic Clinics of North America
Urologic Clinics of North America 医学-泌尿学与肾脏学
CiteScore
4.50
自引率
4.20%
发文量
77
审稿时长
6-12 weeks
期刊介绍: For 30 years, Urologic Clinics of North America has provided important clinical updates on the entire spectrum of urology. Each quarterly issue (February, May, August, and November) focuses on a specific topic, with review articles written by leading experts in the field. Topics include adult and pediatric clinical urology, as well as surgical oncology, radiology, pathology, erectile dysfunction, infertility, incontinence, transplantation, endourology, andrology, female urology, reconstructive surgery, and medical oncology.
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